Phonetic patterns for fuzzy matching in natural language processing

ABSTRACT

A token is extracted from a Natural Language input. A phonetic pattern is computed corresponding to the token, the phonetic pattern including a sound pattern that represents a part of the token when the token is spoken. New data is created from data of the phonetic pattern, the new data including a syllable sequence corresponding to the phonetic pattern. A state of a data storage device is changed by storing the new data in a matrix of syllable sequences corresponding to the token. An option is selected that corresponds to the token by executing a fuzzy matching algorithm using a processor and a memory, the selecting of the option is based on a syllable sequence in the matrix.

TECHNICAL FIELD

The present invention relates generally to a method, system, andcomputer program product for improving Natural Language Processing(NLP). More particularly, the present invention relates to a method,system, and computer program product for phonetic patterns for fuzzymatching in natural language processing.

BACKGROUND

A natural language (NL) is a scripted (written) or a vocalized (spoken)language having a form that is employed by humans for primarilycommunicating with other humans or with systems having a naturallanguage interface.

Natural language processing (NLP) is a technique that facilitatesexchange of information between humans and data processing systems. Forexample, one branch of NLP pertains to transforming human readable orhuman understandable content into machine usable data. For example, NLPengines are presently usable to accept input content such as a newspaperarticle or human speech, and produce structured data, such as an outlineof the input content, most significant and least significant parts, asubject, a reference, dependencies within the content, and the like,from the given content.

An NL input is an input in constructed using a grammar of a naturallanguage and presented in a suitable form, including but not limited totext, audio, and forms thereof, such as transcription from audio speech,machine-generated audio from text. A unit of an NL input is the shortestmeaningful portion of the input. For example, in the English language, aunit would be a word; and words form other larger structures such asphrases, sentences, and paragraphs in the NL input. A unit of an NLinput is also referred to herein as a token.

Presently algorithms are available to enable machines in understandingNL inputs. An essential part of understanding the NL input is repeatedlyand reliably selecting the correct choice from the many likelymachine-interpretations of an NL token. For example, a machine should beable to conclude that “tow-mah-tow” and “tuh-may-tow” are simplydifferent ways of saying “tomato” and when “tow-mah-tow” is presented asan NL token, the correct selection or choice for that token is “tomato”.

The illustrative embodiments recognize that machine-understanding of atoken is sensitive to a number of factors. In some cases, an emphasisplaced on a token or a portion thereof can cause an incorrect selectioncorresponding to the token. In some other cases, a dialect, an accent, alocality of the NL input affects the meaning of the token. Additionally,there can be multiple valid choices corresponding to a token but onlyone of them correct based on the factors involved.

The factors contemplated by the illustrative embodiments are related tothe phonetic variations of a token as described herein. As such, thefactors contemplated by the illustrative embodiments, which affectmachine-understanding of NL tokens, are distinct from misspelling andtypographical errors-type of reasons that affect correct tokenrecognition. Presently, techniques exist to help an NLP machine toselect the correct choice when misspelled tokens are encountered intextual NL inputs. Several misspelled tokens are mapped to the samecorrect word, e.g., misspellings such as “tirminate”, “termate”, and“termenate” are mapped to the correct selection—“terminate”- to assistthe NLP machine to make the correct selection when a misspelled token isencountered.

Some presently used NLP algorithms build large caches of misspellingsmapped to correct spellings. Such caches can be large, but they arestill far from exhaustive. For example, just for the English languagecache, a single eight-character word can theoretically have 26⁸(208,827,064,576) possible variations. Some algorithms in this class ofalgorithms optimize the cache, e.g., by including only the most commonmisspellings. Still, the cache of mappings remains far from complete, isnot scalable, and handles only a limited type of issues—the misspellingsin textual inputs.

Fuzzy matching is another class of algorithms used to map an NL token toa choice or selection from a set of selections. A fuzzy matchingalgorithm (FUZZY MATCHING ALGORITHM) is a string matching algorithm thatuses variations of edit distance algorithms as a means of findingsimilarities between a given token string from textual input and anavailable selection string in a set of selections. Fuzzy matchingalgorithms also operate on textual NL inputs, and are presentlyconfigured for correctly understanding misspelled character strings.

Presently, fuzzy matching algorithms are designed to have a high recallat the cost of sacrificing precision. Recall is a fraction of relevantinstances that are retrieved, and precision is the fraction of retrievedinstances that are relevant. Precision can be seen as a measure ofexactness or quality, whereas recall is a measure of completeness orquantity. Maximum precision indicates no false positives, and maximumrecall indicates no false negatives.

The illustrative embodiments recognize that factors other thanmisspellings in textual inputs are responsible for precision ofunderstanding NL tokens. Such factors are dependent upon the tonal orphonetic characteristics of the token rather than the correctness orincorrectness of the textual spelling of the token.

The illustrative embodiments recognize that a method is needed by whichthe phonetic variations of tokens can be represented in NLP so that thefuzzy matching application increase in precision while keeping therecall characteristic unchanged when making selections corresponding toNL inputs. The illustrative embodiments recognize that the presentlyavailable fuzzy matching algorithms have to be modified to be able touse phonetic characteristics of tokens as additional inputs indetermining the correct selection corresponding to the token.

SUMMARY

The illustrative embodiments provide a method, system, and computerprogram product. An embodiment includes a method that extracts a tokenfrom a Natural Language (NL) input. The embodiment computes a phoneticpattern corresponding to the token, the phonetic pattern comprising asound pattern that represents a part of the token when the token isspoken. The embodiment creates new data from data of the phoneticpattern, the new data comprising a syllable sequence corresponding tothe phonetic pattern. The embodiment changes a state of a data storagedevice by storing the new data in a matrix of syllable sequencescorresponding to the token. The embodiment selects, by executing a fuzzymatching algorithm using a processor and a memory, an option thatcorresponds to the token, wherein the selecting is based on a syllablesequence in the matrix. Thus, the embodiment causes an improvement inthe precision of the fuzzy matching algorithm by selecting a matchingoption for the NL token where the option is selected because the optionalso corresponds to a syllable sequence representative of the token.

Another embodiment further computes a second syllable sequencecorresponding to the phonetic pattern. The embodiment saves the secondsyllable sequence in the matrix. Thus, the embodiment enablesconfiguring a plurality of syllable sequences that is representative ofthe token.

Another embodiment further determines whether a stored phonetic patternin a phonetic repository corresponds to the token. The embodimentselects, responsive to the stored phonetic pattern corresponding to thetoken, the stored phonetic pattern as the phonetic pattern. Thus, theembodiment uses a historically learned phonetic pattern from a previousoccurrence of the token.

Another embodiment further modifies, to form the phonetic pattern, astored phonetic pattern corresponding to a second token in a phoneticrepository, wherein the second token is comparable to the token byhaving a greater than a threshold degree of structural similarity withthe token. Thus, the embodiment uses a phonetic pattern of a comparablebut different token to construct a phonetic pattern of the token.

In another embodiment, the structural similarity exists because at leasta threshold degree of similarity exists between a spelling of the tokenand a spelling of the second token. Thus, the embodiment provides onemanner of selecting the comparable token.

In another embodiment, the structural similarity exists because at leasta threshold degree of similarity exists between a length of a spellingof the token and a length of a spelling of the second token. Thus, theembodiment provides another manner of selecting the comparable token.

In another embodiment, the structural similarity exists because at leasta threshold degree of similarity exists between a sequence of vowels inthe token and a sequence of vowels in the second token. Thus, theembodiment provides another manner of selecting the comparable token.Thus, the embodiment provides another manner of selecting the comparabletoken.

In another embodiment, the sound pattern comprises a phoneticrepresentation, wherein the structural similarity exists because atleast a threshold degree of similarity exists between a number ofphonetic representations in the token and a number of phoneticrepresentations in the second token. Thus, the embodiment providesanother manner of selecting the comparable token.

In another embodiment, the sound pattern comprises a phoneticrepresentation, wherein the structural similarity exists because atleast a threshold degree of similarity exists between an order ofphonetic representations in the token and an order of phoneticrepresentations in the second token. Thus, the embodiment providesanother manner of selecting the comparable token.

In another embodiment, the sound pattern comprises a phoneticrepresentation, wherein the structural similarity exists because atleast a threshold degree of similarity exists between an emphasis on thephonetic representation in the token and an emphasis on the phoneticrepresentation in the second token. Thus, the embodiment providesanother manner of selecting the comparable token.

Another embodiment further computes the phonetic pattern from a secondphonetic pattern by adding a second sound pattern to the second phoneticpattern. Thus, the embodiment provides a manner of modifying thephonetic pattern of the comparable token to create the phonetic patternof the token.

Another embodiment further computes the phonetic pattern from a secondphonetic pattern by removing a second sound pattern from the secondphonetic pattern. Thus, the embodiment provides another manner ofmodifying the phonetic pattern of the comparable token to create thephonetic pattern of the token.

Another embodiment further computes the phonetic pattern from a secondphonetic pattern by replacing a second sound pattern in the secondphonetic pattern with the sound pattern. Thus, the embodiment providesanother manner of modifying the phonetic pattern of the comparable tokento create the phonetic pattern of the token.

Another embodiment further computes the phonetic pattern from a secondphonetic pattern by changing a sequence of sound patterns in the secondphonetic pattern. Thus, the embodiment provides another manner ofmodifying the phonetic pattern of the comparable token to create thephonetic pattern of the token.

Another embodiment further computes the phonetic pattern from a secondphonetic pattern by changing a duration of a second sound pattern in thesecond phonetic pattern. Thus, the embodiment provides another manner ofmodifying the phonetic pattern of the comparable token to create thephonetic pattern of the token.

Another embodiment further computes the phonetic pattern from a secondphonetic pattern by changing an emphasis on a second sound pattern inthe second phonetic pattern. Thus, the embodiment provides anothermanner of modifying the phonetic pattern of the comparable token tocreate the phonetic pattern of the token.

Another embodiment further computes a set of phonetic patternscorresponding to the token, the phonetic pattern being a member of theset of phonetic patterns. Thus, the embodiment provides that a pluralityof phonetic patterns can represent the token.

In another embodiment, the sound pattern represents the part of thetoken when the token is spoken in a dialect of a language. Thus, theembodiment provides that a plurality of phonetic patterns can representthe token such that a phonetic pattern is based on sound patternscreated by speaking in a dialect.

In another embodiment, the sound pattern represents the part of thetoken when the token is spoken with an accent in a language. Thus, theembodiment provides that a plurality of phonetic patterns can representthe token such that a phonetic pattern is based on sound patternscreated by speaking with an accent.

In another embodiment, the sound pattern represents the part of thetoken when the token is spoken in a language with a speech-peculiarityof a speaker. Thus, the embodiment provides that a plurality of phoneticpatterns can represent the token such that a phonetic pattern is basedon sound patterns created by a speaker's speaking-relatedidiosyncrasies.

In another embodiment, the token comprises a shortest meaningful unit ofspeech in the NL input. Thus, the embodiment provides a specific type oflinguistic construct that can form a token.

An embodiment includes a computer usable program product. The computerusable program product includes a computer-readable storage device, andprogram instructions stored on the storage device.

An embodiment includes a computer system. The computer system includes aprocessor, a computer-readable memory, and a computer-readable storagedevice, and program instructions stored on the storage device forexecution by the processor via the memory.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofthe illustrative embodiments when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented;

FIG. 2 depicts a block diagram of a data processing system in whichillustrative embodiments may be implemented;

FIG. 3 depicts a block diagram of an example configuration for phoneticpatterns for fuzzy matching in natural language processing in accordancewith an illustrative embodiment;

FIG. 4 depicts a block diagram of an example application for phoneticpatterns for fuzzy matching in natural language processing in accordancewith an illustrative embodiment;

FIG. 5 depicts a flowchart of an example process for phonetic patternsfor fuzzy matching in natural language processing in accordance with anillustrative embodiment; and

FIG. 6 depicts a flowchart of an example process for machine learning toimprove phonetic pattern selection for NL tokens in accordance with anillustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments recognize that the presently availabletools or solutions do not address these needs/problems or provideadequate solutions for these needs/problems. The illustrativeembodiments used to describe the invention generally address and solvethe above-described problems and other related problems by phoneticpatterns for fuzzy matching in natural language processing.

An embodiment can be implemented as a software application. Theapplication implementing an embodiment, or one or more componentsthereof, can be configured as a modification of an existing applicationimplementing an fuzzy matching algorithm (fuzzy matchingapplication)—i.e., a native application in the fuzzy matchingapplication, as an application executing in a data processing systemcommunicating with an existing fuzzy matching application over ashort-range radio network such as Bluetooth, over a wired or wirelinelocal area network (LAN)—i.e., a local application on the LAN, as anapplication executing in a data processing system communicating with anexisting fuzzy matching application over a wide area network (WAN)—i.e.,a remote application on the WAN, as a separate application that operatesin conjunction with an existing fuzzy matching application in otherways, a standalone application, or some combination thereof.

Within the scope of the illustrative embodiments, a phoneticrepresentation is a sound pattern that represents all or a part of a NLtoken when the token is spoken. A syllable is phonological buildingblock, to wit, a sound used in a speech in a given language that can berepresented using one or more vowels of that language.

In accordance with an illustrative embodiment, a token is extracted froman NL input. The embodiment determines one or more distinct phoneticrepresentations (distinct phonetic sounds) that together from a phoneticpattern, and the phonetic pattern represents the token. For example, onemanner of representing an example token—“tomato”—is where “tow” “mah”and “tow” form a set of three phonetic representations that togetherform the phonetic pattern “tow-mah-tow”, which represents token“tomato”. Another example manner of representing the exampletoken—“tomato”—is where “tuh” “may” and “tow” form a different set ofthree phonetic representations that together form a second phoneticpattern “tuh-may-tow”, which also represents token “tomato”.

In other words, given a token, the embodiment constructs one or morephonetic patterns using corresponding sets of phonetic representations,such that any of the phonetic patterns can possibly represent the giventoken.

An embodiment stores a phonetic pattern of a token in a repository—thephonetic repository. Over time, upon encountering several tokens andseveral instances of the same token under different circumstances, thephonetic repository becomes populated with one or more phonetic patternscorresponding to one or more tokens.

In one embodiment, given a token, a set of phonetic patternscorresponding to the token are available in the phonetic repository.When a phonetic pattern of the token is available is the repository, thephonetic pattern is extracted into the set of phonetic patterns for thattoken.

In another embodiment, a phonetic pattern of a different token, which iscomparable or similar to the given token in its phonetic structure, maybe available in the repository. An embodiment computes a phoneticpattern of the token from the phonetic pattern of the comparable token.

For example, a phonetic pattern for tomato may not be available but aphonetic pattern for “potato” might be available in the repository. Anembodiment determines the structural similarity between a given tokenand a comparable token by comparing for the two tokens the spelling, thelength of the spelling, arrangement of the vowels therein, the number ofphonetic representations therein, the order of phonetic representationstherein, the emphasis on a particular phonetic representation, or somecombination of these and other such characteristics. When the structuralsimilarities between the given token and a comparable token exceeds athreshold level of similarity, the embodiment uses the phonetic patternof the comparable token from the repository and computes a phoneticpattern of the given token from that phonetic pattern.

In an embodiment, the computing of the phonetic pattern of the giventoken can include adding a phonetic representation to the phoneticpattern of the comparable token, deleting a phonetic representation fromthe phonetic pattern of the comparable token, replacing a phoneticrepresentation in the phonetic pattern of the comparable token with adifferent phonetic representation, changing a sequence of phoneticrepresentations in the phonetic pattern of the comparable token,compressing a duration during which the phonetic pattern of thecomparable token is spoken or sounded out, expanding a duration duringwhich the phonetic pattern of the comparable token is spoken or soundedout, adding an emphasis to a phonetic representation in the phoneticpattern of the comparable token, removing an emphasis from a phoneticrepresentation in the phonetic pattern of the comparable token, changingan emphasis from one phonetic representation to a different phoneticrepresentation in the phonetic pattern of the comparable token, and acombination of these and many other aspects depending upon the language,dialect, accent, context, speaker's peculiarities, etc.

Regardless of the form in which the NL input is provided—e.g., textual,audio, or other forms described herein, an embodiment can construct aset of phonetic patterns corresponding to a token using phoneticpatterns for the token from the repository and phonetic patterns ofcomparable tokens from the repositories. When the token is extractedfrom an NL input that is in audio form, a phonetic pattern for a tokencan also be constructed by segmenting the audio of the token intoconstituent phonetic representations.

Once a phonetic pattern is available for a token, an embodiment computesa syllable corresponding to each phonetic representation in the phoneticpattern. Thus, the embodiment transforms a phonetic pattern into asequence of syllables. The transformation of a phonetic representationinto a syllable can be performed by matching a sound present in thephonetic representation with a sound made by sounding out a syllable.For example, the phonetic pattern “tow-mah-tow” includes the sequence ofsounds “oh” (

), “ah” (

:), and “oh” (

). Thus, the sequence of syllables corresponding to the phonetic patternof the token is “

:

”.

Operating in this manner, the embodiment constructs a syllable sequencefor each phonetic pattern corresponding to the token. More than onesyllable sequences may be possible for a single phonetic pattern. Theset of syllable sequence thus created forms a matrix of syllablesequences.

An embodiment modifies an existing fuzzy matching application to acceptthe matrix of syllable sequences as a supporting input together with theNL input. The modified fuzzy matching application uses a syllablesequence in the matrix in the fuzzy matching algorithm to identify asuitable selection corresponding to a token. The selections identifiedusing the syllable sequences exhibit a higher precision than theselections identified using only the prior-art fuzzy matching algorithmwithout using the syllable sequences.

The manner of phonetic patterns for fuzzy matching in natural languageprocessing described herein is unavailable in the presently availablemethods. A method of an embodiment described herein, when implemented toexecute on a device or data processing system, comprises substantialadvancement of the functionality of that device or data processingsystem in improving a precision of a fuzzy matching application withoutdisturbing the fuzzy matching application's recall characteristics inthe technical field of NLP.

The illustrative embodiments are described with respect to certain typesof NL, NL inputs, grammars, tokens, phonetic representations, phoneticpatterns, syllables, syllable sequences, matrices, fuzzy matchingalgorithms, fuzzy matching applications, devices, data processingsystems, environments, components, and applications only as examples.Any specific manifestations of these and other similar artifacts are notintended to be limiting to the invention. Any suitable manifestation ofthese and other similar artifacts can be selected within the scope ofthe illustrative embodiments.

Furthermore, the illustrative embodiments may be implemented withrespect to any type of data, data source, or access to a data sourceover a data network. Any type of data storage device may provide thedata to an embodiment of the invention, either locally at a dataprocessing system or over a data network, within the scope of theinvention. Where an embodiment is described using a mobile device, anytype of data storage device suitable for use with the mobile device mayprovide the data to such embodiment, either locally at the mobile deviceor over a data network, within the scope of the illustrativeembodiments.

The illustrative embodiments are described using specific code, designs,architectures, protocols, layouts, schematics, and tools only asexamples and are not limiting to the illustrative embodiments.Furthermore, the illustrative embodiments are described in someinstances using particular software, tools, and data processingenvironments only as an example for the clarity of the description. Theillustrative embodiments may be used in conjunction with othercomparable or similarly purposed structures, systems, applications, orarchitectures. For example, other comparable mobile devices, structures,systems, applications, or architectures therefor, may be used inconjunction with such embodiment of the invention within the scope ofthe invention. An illustrative embodiment may be implemented inhardware, software, or a combination thereof.

The examples in this disclosure are used only for the clarity of thedescription and are not limiting to the illustrative embodiments.Additional data, operations, actions, tasks, activities, andmanipulations will be conceivable from this disclosure and the same arecontemplated within the scope of the illustrative embodiments.

Any advantages listed herein are only examples and are not intended tobe limiting to the illustrative embodiments. Additional or differentadvantages may be realized by specific illustrative embodiments.Furthermore, a particular illustrative embodiment may have some, all, ornone of the advantages listed above.

With reference to the figures and in particular with reference to FIGS.1 and 2, these figures are example diagrams of data processingenvironments in which illustrative embodiments may be implemented. FIGS.1 and 2 are only examples and are not intended to assert or imply anylimitation with regard to the environments in which differentembodiments may be implemented. A particular implementation may makemany modifications to the depicted environments based on the followingdescription.

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented. Data processingenvironment 100 is a network of computers in which the illustrativeembodiments may be implemented. Data processing environment 100 includesnetwork 102. Network 102 is the medium used to provide communicationslinks between various devices and computers connected together withindata processing environment 100. Network 102 may include connections,such as wire, wireless communication links, or fiber optic cables.

Clients or servers are only example roles of certain data processingsystems connected to network 102 and are not intended to exclude otherconfigurations or roles for these data processing systems. Server 104and server 106 couple to network 102 along with storage unit 108.Software applications may execute on any computer in data processingenvironment 100. Clients 110, 112, and 114 are also coupled to network102. A data processing system, such as server 104 or 106, or client 110,112, or 114 may contain data and may have software applications orsoftware tools executing thereon.

Only as an example, and without implying any limitation to sucharchitecture, FIG. 1 depicts certain components that are usable in anexample implementation of an embodiment. For example, servers 104 and106, and clients 110, 112, 114, are depicted as servers and clients onlyas examples and not to imply a limitation to a client-serverarchitecture. As another example, an embodiment can be distributedacross several data processing systems and a data network as shown,whereas another embodiment can be implemented on a single dataprocessing system within the scope of the illustrative embodiments. Dataprocessing systems 104, 106, 110, 112, and 114 also represent examplenodes in a cluster, partitions, and other configurations suitable forimplementing an embodiment.

Device 132 is an example of a device described herein. For example,device 132 can take the form of a smartphone, a tablet computer, alaptop computer, client 110 in a stationary or a portable form, awearable computing device, or any other suitable device. Any softwareapplication described as executing in another data processing system inFIG. 1 can be configured to execute in device 132 in a similar manner.Any data or information stored or produced in another data processingsystem in FIG. 1 can be configured to be stored or produced in device132 in a similar manner.

Application 105 implements an embodiment described herein. fuzzymatching application 107 is a modified fuzzy matching application whichincludes a modified fuzzy matching algorithm where the modified fuzzymatching algorithm has been modified to accept a matrix of syllablesequences as an additional input for identifying a correct selectioncorresponding to a token in an NL input. Phonetic repository 109includes one or more phonetic patterns for one or more tokens in one ormore languages, as described herein.

Servers 104 and 106, storage unit 108, and clients 110, 112, and 114,and device 132 may couple to network 102 using wired connections,wireless communication protocols, or other suitable data connectivity.Clients 110, 112, and 114 may be, for example, personal computers ornetwork computers.

In the depicted example, server 104 may provide data, such as bootfiles, operating system images, and applications to clients 110, 112,and 114. Clients 110, 112, and 114 may be clients to server 104 in thisexample. Clients 110, 112, 114, or some combination thereof, may includetheir own data, boot files, operating system images, and applications.Data processing environment 100 may include additional servers, clients,and other devices that are not shown.

In the depicted example, data processing environment 100 may be theInternet. Network 102 may represent a collection of networks andgateways that use the Transmission Control Protocol/Internet Protocol(TCP/IP) and other protocols to communicate with one another. At theheart of the Internet is a backbone of data communication links betweenmajor nodes or host computers, including thousands of commercial,governmental, educational, and other computer systems that route dataand messages. Of course, data processing environment 100 also may beimplemented as a number of different types of networks, such as forexample, an intranet, a local area network (LAN), or a wide area network(WAN). FIG. 1 is intended as an example, and not as an architecturallimitation for the different illustrative embodiments.

Among other uses, data processing environment 100 may be used forimplementing a client-server environment in which the illustrativeembodiments may be implemented. A client-server environment enablessoftware applications and data to be distributed across a network suchthat an application functions by using the interactivity between aclient data processing system and a server data processing system. Dataprocessing environment 100 may also employ a service orientedarchitecture where interoperable software components distributed acrossa network may be packaged together as coherent business applications.Data processing environment 100 may also take the form of a cloud, andemploy a cloud computing model of service delivery for enablingconvenient, on-demand network access to a shared pool of configurablecomputing resources (e.g. networks, network bandwidth, servers,processing, memory, storage, applications, virtual machines, andservices) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of the service.

With reference to FIG. 2, this figure depicts a block diagram of a dataprocessing system in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as servers104 and 106, or clients 110, 112, and 114 in FIG. 1, or another type ofdevice in which computer usable program code or instructionsimplementing the processes may be located for the illustrativeembodiments.

Data processing system 200 is also representative of a data processingsystem or a configuration therein, such as data processing system 132 inFIG. 1 in which computer usable program code or instructionsimplementing the processes of the illustrative embodiments may belocated. Data processing system 200 is described as a computer only asan example, without being limited thereto. Implementations in the formof other devices, such as device 132 in FIG. 1, may modify dataprocessing system 200, such as by adding a touch interface, and eveneliminate certain depicted components from data processing system 200without departing from the general description of the operations andfunctions of data processing system 200 described herein.

In the depicted example, data processing system 200 employs a hubarchitecture including North Bridge and memory controller hub (NB/MCH)202 and South Bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 arecoupled to North Bridge and memory controller hub (NB/MCH) 202.Processing unit 206 may contain one or more processors and may beimplemented using one or more heterogeneous processor systems.Processing unit 206 may be a multi-core processor. Graphics processor210 may be coupled to NB/MCH 202 through an accelerated graphics port(AGP) in certain implementations.

In the depicted example, local area network (LAN) adapter 212 is coupledto South Bridge and I/O controller hub (SB/ICH) 204. Audio adapter 216,keyboard and mouse adapter 220, modem 222, read only memory (ROM) 224,universal serial bus (USB) and other ports 232, and PCI/PCIe devices 234are coupled to South Bridge and I/O controller hub 204 through bus 238.Hard disk drive (HDD) or solid-state drive (SSD) 226 and CD-ROM 230 arecoupled to South Bridge and I/O controller hub 204 through bus 240.PCI/PCIe devices 234 may include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbinary input/output system (BIOS). Hard disk drive 226 and CD-ROM 230may use, for example, an integrated drive electronics (IDE), serialadvanced technology attachment (SATA) interface, or variants such asexternal-SATA (eSATA) and micro-SATA (mSATA). A super I/O (SIO) device236 may be coupled to South Bridge and I/O controller hub (SB/ICH) 204through bus 238.

Memories, such as main memory 208, ROM 224, or flash memory (not shown),are some examples of computer usable storage devices. Hard disk drive orsolid state drive 226, CD-ROM 230, and other similarly usable devicesare some examples of computer usable storage devices including acomputer usable storage medium.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within dataprocessing system 200 in FIG. 2. The operating system may be acommercially available operating system for any type of computingplatform, including but not limited to server systems, personalcomputers, and mobile devices. An object oriented or other type ofprogramming system may operate in conjunction with the operating systemand provide calls to the operating system from programs or applicationsexecuting on data processing system 200.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs, such as application 105 and/ormodified fuzzy matching application 107 in FIG. 1, are located onstorage devices, such as in the form of code 226A on hard disk drive226, and may be loaded into at least one of one or more memories, suchas main memory 208, for execution by processing unit 206. The processesof the illustrative embodiments may be performed by processing unit 206using computer implemented instructions, which may be located in amemory, such as, for example, main memory 208, read only memory 224, orin one or more peripheral devices.

Furthermore, in one case, code 226A may be downloaded over network 201Afrom remote system 201B, where similar code 201C is stored on a storagedevice 201D. in another case, code 226A may be downloaded over network201A to remote system 201B, where downloaded code 201C is stored on astorage device 201D.

The hardware in FIGS. 1-2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS.1-2. In addition, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system.

In some illustrative examples, data processing system 200 may be apersonal digital assistant (PDA), which is generally configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data. A bus system may comprise one or morebuses, such as a system bus, an I/O bus, and a PCI bus. Of course, thebus system may be implemented using any type of communications fabric orarchitecture that provides for a transfer of data between differentcomponents or devices attached to the fabric or architecture.

A communications unit may include one or more devices used to transmitand receive data, such as a modem or a network adapter. A memory may be,for example, main memory 208 or a cache, such as the cache found inNorth Bridge and memory controller hub 202. A processing unit mayinclude one or more processors or CPUs.

The depicted examples in FIGS. 1-2 and above-described examples are notmeant to imply architectural limitations. For example, data processingsystem 200 also may be a tablet computer, laptop computer, or telephonedevice in addition to taking the form of a mobile or wearable device.

Where a computer or data processing system is described as a virtualmachine, a virtual device, or a virtual component, the virtual machine,virtual device, or the virtual component operates in the manner of dataprocessing system 200 using virtualized manifestation of some or allcomponents depicted in data processing system 200. For example, in avirtual machine, virtual device, or virtual component, processing unit206 is manifested as a virtualized instance of all or some number ofhardware processing units 206 available in a host data processingsystem, main memory 208 is manifested as a virtualized instance of allor some portion of main memory 208 that may be available in the hostdata processing system, and disk 226 is manifested as a virtualizedinstance of all or some portion of disk 226 that may be available in thehost data processing system. The host data processing system in suchcases is represented by data processing system 200.

With reference to FIG. 3, this figure depicts a block diagram of anexample configuration for phonetic patterns for fuzzy matching innatural language processing in accordance with an illustrativeembodiment. Application 302 is an example of application 105 in FIG. 1.Fuzzy matching application 304 is an example of modified fuzzy matchingapplication 107 in FIG. 1. Fuzzy matching application 304 comprisesprior-art fuzzy matching algorithm 304A and phonetic input processingcomponent 304B according to an embodiment. Phonetic repository 306 is anexample of phonetic repository 109 in FIG. 1.

NL input 308 comprises one or more forms of NL data as described herein.NL input 308 includes one or more tokens, e.g., words.

Application 302 suitably parses NL input 308 to extract a token from NLinput 308. Using one or more phonetic patterns from phonetic repository306 and/or audio data from NL input 308 if available, application 302constructs one or more phonetic patterns corresponding to the token.Application 302 computes one or more syllable sequences corresponding toeach phonetic pattern and forms syllable sequence matrix 310. A syllablesequence, e.g., syllable sequence 312 in matrix 310 comprises a sequenceof one or more syllables, such as syllables 312A, 312B . . . 312C.

Phonetic input processing component 304B receives NL input 308 as oneinput, and matrix 310 from application 302 as an additional input.Component 304B causes fuzzy matching algorithm 304A to use the syllablesequences in the selection/choice identification process correspondingto a given token. Fuzzy matching application 304 outputs selection 314with a greater precision and at least the same recall as compared to aselection that would have been output from fuzzy matching algorithm 304Aalone.

In one embodiment, the selection output of modified fuzzy matchingapplication 304 is used to train application 302. For example, if aseparate process (not shown) determines that selection 314 is a probableselection for a given token, machine learning feedback 316 causesapplication 302 to increase or reinforce those phonetic patterns thatcorrespond to selection 314 within a threshold degree of correspondencesuch that those phonetic patterns are produced again (or givencomparatively more weight) for the same or similar token in a subsequentoccurrence of the token. If a phonetic pattern was constructed that doesnot correspond to selection 314 within a threshold degree ofcorrespondence, feedback 316 causes application 302 to decrease orweaken those phonetic patterns such that those phonetic patterns are notproduced (or given comparatively less weight) for the same or similartoken in a subsequent occurrence of the token.

With reference to FIG. 4, this figure depicts a block diagram of anexample application for phonetic patterns for fuzzy matching in naturallanguage processing in accordance with an illustrative embodiment.Application 402 can be used as application 302 in FIG. 3.

Component 404 extracts a token from an NL input, e.g., by parsing NLinput 308 according to a grammar of the language of input 308. Component406 computes a phonetic pattern for the token in any one or more mannersdescribed herein.

Component 408 computes a syllable sequence corresponding to a phoneticpattern. component 408 outputs a matrix of syllable sequences, e.g.,matrix 310, corresponding to the token.

Component 410 may be configured to operate as phonetic input processingcomponent 304B in FIG. 3. Component 410 processes the matrix of syllablesequences to use in the modified fuzzy matching application, e.g., infuzzy matching application 304.

Component 412 receives a feedback of the selection made by the modifiedfuzzy matching application, e.g., feedback 316. Component 414 implementsa suitable process for classifying the selection as probable (validselection in the context of NL input 308) or improbable (invalidselection in the context of NL input 308).

Component 416 reinforces those phonetic patterns of the token whichcorrespond to a probable selection. Component 418 weakens those phoneticpatterns of the token which correspond to an improbable selection.Component 420 manages the phonetic repository, e.g., by adding a newphonetic pattern, reinforcing a phonetic pattern, weakening a phoneticpattern, removing a phonetic pattern, or otherwise manipulating phoneticpatterns in the repository.

With reference to FIG. 5, this figure depicts a flowchart of an exampleprocess for phonetic patterns for fuzzy matching in natural languageprocessing in accordance with an illustrative embodiment. Process 500can be implemented in application 402 in FIG. 4.

The application receives an NL input (block 502). The applicationselects a unit of speech, to wit, a token, from the input (block 504).For the selected token, the application performs one or more of blocks506, 508, and 510. For example, the application determines whether aphonetic pattern for the token exists in a phonetic repository (block506); the application determines whether a phonetic pattern of acomparable token exists in the repository (block 508); and/or theapplication computes a phonetic pattern of the token based on the actualor estimated audio of the token in the NL input (block 510). If/when theapplication executes block 510 to compute a phonetic pattern of thetoken based on the actual or estimated audio of the token in the NLinput, the application updates the phonetic repository with the computedphonetic pattern (block 512).

If a phonetic pattern of the token exists in the repository (“Yes” pathof block 506), the application computes one or more syllable sequencescorresponding to the phonetic pattern (block 514). If a phonetic patternof the token does not exist in the repository (“No” path of block 506),the application proceeds to block 508.

If a phonetic pattern of a comparable token exists in the repository(“Yes” path of block 508), the application computes a phonetic patternof the token based on the phonetic pattern of the comparable token(block 516). The application updates the repository with the computedphonetic pattern for the token at block 512.

The application determines whether more phonetic patterns of the tokenhave to be computed in a similar manner (block 518). If more phoneticpatterns of the token have to be computed (“Yes” path of block 518), theapplication returns to any of blocks 506, 508, and 510. If no morephonetic patterns have to be computed for the token (“No” path of block518), the application generates a matrix of the computed syllablesequences (block 520). Optionally, the application may preprocess thematrix for consumption as an additional input into a modified fuzzymatching application (block 522).

The application provides the matrix as an additional input to themodified fuzzy matching application (block 524). The application causesthe modified fuzzy matching application to output a selectioncorresponding to the token with an improved precision (block 526). Theapplication may end process 500 thereafter.

If no more phonetic patterns have to be computed for the token (“No”path of block 518), the application may also determine in parallelwhether more tokens have to be processed from the NL input (block 528).If more tokens have to be processed from the NL input (“Yes” path ofblock 528), the application returns to block 504 and selects anothertoken. If no more tokens have to be processed from the NL input (“No”path of block 528), the application ends process 500 thereafter.

With reference to FIG. 6, this figure depicts a flowchart of an exampleprocess for machine learning to improve phonetic pattern selection forNL tokens in accordance with an illustrative embodiment. Process 600 canbe implemented in application 402 in FIG. 4.

The application receives a selection made by a modified fuzzy matchingapplication corresponding to a token (block 602). The applicationdetermines whether, given the NL input context, the selection isprobable (block 604). If the selection is probable (“Probable” path ofblock 604), the application reinforces those phonetic patterns of thetoken which correspond to the selection (block 606). The applicationends process 600 thereafter.

If the selection is improbable (“Improbable” path of block 604), theapplication weakens those phonetic patterns of the token whichcorrespond to the selection (block 608). The application ends process600 thereafter.

Thus, a computer implemented method, system or apparatus, and computerprogram product are provided in the illustrative embodiments forphonetic patterns for fuzzy matching in natural language processing andother related features, functions, or operations. Where an embodiment ora portion thereof is described with respect to a type of device, thecomputer implemented method, system or apparatus, the computer programproduct, or a portion thereof, are adapted or configured for use with asuitable and comparable manifestation of that type of device.

Where an embodiment is described as implemented in an application, thedelivery of the application in a Software as a Service (SaaS) model iscontemplated within the scope of the illustrative embodiments. In a SaaSmodel, the capability of the application implementing an embodiment isprovided to a user by executing the application in a cloudinfrastructure. The user can access the application using a variety ofclient devices through a thin client interface such as a web browser(e.g., web-based e-mail), or other light-weight client-applications. Theuser does not manage or control the underlying cloud infrastructureincluding the network, servers, operating systems, or the storage of thecloud infrastructure. In some cases, the user may not even manage orcontrol the capabilities of the SaaS application. In some other cases,the SaaS implementation of the application may permit a possibleexception of limited user-specific application configuration settings.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, including but not limited tocomputer-readable storage devices as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general-purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A method comprising: extracting a token from aNatural Language (NL) input; computing a phonetic pattern correspondingto the token, the phonetic pattern comprising a sound pattern thatrepresents a part of the token when the token is spoken; creating newdata from data of the phonetic pattern, the new data comprising asyllable sequence corresponding to the phonetic pattern; changing astate of a data storage device by storing the new data in a matrix ofsyllable sequences corresponding to the token; selecting, by executing afuzzy matching algorithm using a processor and a memory, an option thatcorresponds to the token, wherein the selecting is based on a syllablesequence in the matrix.
 2. The method of claim 1, further comprising:computing a second syllable sequence corresponding to the phoneticpattern; and saving the second syllable sequence in the matrix.
 3. Themethod of claim 1, further comprising: determining whether a storedphonetic pattern in a phonetic repository corresponds to the token;selecting, responsive to the stored phonetic pattern corresponding tothe token, the stored phonetic pattern as the phonetic pattern.
 4. Themethod of claim 1, further comprising: modifying, to form the phoneticpattern, a stored phonetic pattern corresponding to a second token in aphonetic repository, wherein the second token is comparable to the tokenby having a greater than a threshold degree of structural similaritywith the token.
 5. The method of claim 4, wherein the structuralsimilarity exists because at least a threshold degree of similarityexists between a spelling of the token and a spelling of the secondtoken.
 6. The method of claim 4, wherein the structural similarityexists because at least a threshold degree of similarity exists betweena length of a spelling of the token and a length of a spelling of thesecond token.
 7. The method of claim 4, wherein the structuralsimilarity exists because at least a threshold degree of similarityexists between a sequence of vowels in the token and a sequence ofvowels in the second token.
 8. The method of claim 4, wherein the soundpattern comprises a phonetic representation, wherein the structuralsimilarity exists because at least a threshold degree of similarityexists between a number of phonetic representations in the token and anumber of phonetic representations in the second token.
 9. The method ofclaim 4, wherein the sound pattern comprises a phonetic representation,wherein the structural similarity exists because at least a thresholddegree of similarity exists between an order of phonetic representationsin the token and an order of phonetic representations in the secondtoken.
 10. The method of claim 4, wherein the sound pattern comprises aphonetic representation, wherein the structural similarity existsbecause at least a threshold degree of similarity exists between anemphasis on the phonetic representation in the token and an emphasis onthe phonetic representation in the second token.
 11. The method of claim1, further comprising: computing the phonetic pattern from a secondphonetic pattern by adding a second sound pattern to the second phoneticpattern.
 12. The method of claim 1, further comprising: computing thephonetic pattern from a second phonetic pattern by removing a secondsound pattern from the second phonetic pattern.
 13. The method of claim1, further comprising: computing the phonetic pattern from a secondphonetic pattern by replacing a second sound pattern in the secondphonetic pattern with the sound pattern.
 14. The method of claim 1,further comprising: computing the phonetic pattern from a secondphonetic pattern by changing a sequence of sound patterns in the secondphonetic pattern.
 15. The method of claim 1, further comprising:computing the phonetic pattern from a second phonetic pattern bychanging a duration of a second sound pattern in the second phoneticpattern.
 16. The method of claim 1, further comprising: computing thephonetic pattern from a second phonetic pattern by changing an emphasison a second sound pattern in the second phonetic pattern.
 17. The methodof claim 1, further comprising: computing a set of phonetic patternscorresponding to the token, the phonetic pattern being a member of theset of phonetic patterns.
 18. The method of claim 1, wherein the soundpattern represents the part of the token when the token is spoken in adialect of a language.
 19. The method of claim 1, wherein the soundpattern represents the part of the token when the token is spoken withan accent in a language.
 20. The method of claim 1, wherein the soundpattern represents the part of the token when the token is spoken in alanguage with a speech-peculiarity of a speaker.
 21. The method of claim1, wherein the token comprises a shortest meaningful unit of speech inthe NL input.
 22. A computer usable program product comprising acomputer-readable storage device, and program instructions stored on thestorage device, the stored program instructions comprising: programinstructions to extract a token from a Natural Language (NL) input;program instructions to compute a phonetic pattern corresponding to thetoken, the phonetic pattern comprising a sound pattern that represents apart of the token when the token is spoken; program instructions tocreate new data from data of the phonetic pattern, the new datacomprising a syllable sequence corresponding to the phonetic pattern;program instructions to change a state of a data storage device bystoring the new data in a matrix of syllable sequences corresponding tothe token; program instructions to select, by executing a fuzzy matchingalgorithm using a processor and a memory, an option that corresponds tothe token, wherein the selecting is based on a syllable sequence in thematrix.
 23. The computer usable program product of claim 22, wherein thecomputer usable code is stored in a computer readable storage device ina data processing system, and wherein the computer usable code istransferred over a network from a remote data processing system.
 24. Thecomputer usable program product of claim 22, wherein the computer usablecode is stored in a computer readable storage device in a server dataprocessing system, and wherein the computer usable code is downloadedover a network to a remote data processing system for use in a computerreadable storage device associated with the remote data processingsystem.
 25. A computer system comprising a processor, acomputer-readable memory, and a computer-readable storage device, andprogram instructions stored on the storage device for execution by theprocessor via the memory, the stored program instructions comprising:program instructions to extract a token from a Natural Language (NL)input; program instructions to compute a phonetic pattern correspondingto the token, the phonetic pattern comprising a sound pattern thatrepresents a part of the token when the token is spoken; programinstructions to create new data from data of the phonetic pattern, thenew data comprising a syllable sequence corresponding to the phoneticpattern; program instructions to change a state of a data storage deviceby storing the new data in a matrix of syllable sequences correspondingto the token; program instructions to select, by executing a fuzzymatching algorithm using a processor and a memory, an option thatcorresponds to the token, wherein the selecting is based on a syllablesequence in the matrix.